633 results about "Location prediction" patented technology

An energy distribution may include a server and one or more databases. The system may communicate with an energy provider to receive energy provider data, at least one information collector to receive information collector data such as individualized energy usage data, customer preferences, and customer or location characteristics, and the one or more databases for receiving data for optimization. The system may calculate a cost of service or avoided cost using at least one of the individualized energy usage data and a system generation cost at a nearest bus. The system may also forecast individualized demand by end-use, individualized demand by location, energy prices, or energy costs. The system may optimize energy distribution, energy use, cost of service, or avoided cost using the forecasted individualized demand by end-use, the forecasted individualized demand by location, the forecasted energy prices, and the forecasted energy costs.

The invention relates to the technical field of data mining, in particular to a location prediction system and a method based on historical track data mining. In the method, the location prediction problem is decomposed into two sub-problems of offline track mining and online mode matching, wherein the offline track mining mainly adopts the data mining technology to process and analyze the historical track data of a user; and the online mode matching is mainly based on the online motion conditions of the user and the mined motion mode to perform matching and searching. According to the invention, various needs based on the location-based services (such as location-based advertisement, recommendation, reminding and other services) are effectively met, and the problems of the prior art are solved.

The present application describes a computer-implemented method and system for obtaining position information for a moving mobile device with increased accuracy and reduced power consumption. The subject of the present application combines information from a GPS location sensor with information from MEMS devices such as an acceleration detector and a gyroscope using statistical analysis techniques such as a Kalman filter to estimate the location of the device with greater accuracy while using numerical methods such as the Newton-Raphson Method to minimize power consumption. Minimizing power consumption is possible because GPS signals sampled at a lower rate can conserve power, while GPS sampled at a lower rate and working together with MEMS devices can achieve the same level of location prediction accuracy as a GPS alone sampled at a higher rate.

A system and method for predicting the location of a vehicle in an electronic toll collection system employing a wide area communication protocol. The vehicle includes a transponder that sends reports regarding the position of the vehicle and the time at which the position was determined. The system includes a vehicle position predictor for estimating the future position of the vehicle within a roadway based on two or more reports of past positions and the times at which they were recorded. Speed data or other data impacting likely future position may also be reported and factored into the estimate. The estimate of future position may be used in connection with triggering enforcement measures, timing a toll transaction, integrating wide area toll communications into a legacy toll transaction system, or for other applications.

A license plate recognition system may include a vehicle and an analysis server. The vehicle may include an image capture device and an on-board device. The image capture device may capture a vehicle image that includes a license plate of a target vehicle. The on-board device may capture various vehicle data of the target vehicle, such as speed, direction, and location. The analysis server may receive and process the vehicle image and / or vehicle data to determine whether alert criteria are met, e.g., if the target vehicle is stolen. If so, the analysis server may send an alert indication to the vehicle that may include a possible current location of the target vehicle.

The invention discloses a target detection and recognition method based on deep learning. A model is built by utilizing a caffe platform in a Linux system, and consists of convolution layers, pooling layers, RPN layers, an ROIs layer, an ROI Pooling layer, fully-connected layers, a Sparse PCA layer and prediction window and prediction category output layers. A multi-RPN-layer fusion strategy is provided by the invention, and the detection capacity for a target of each scale is enhanced; the new Spares PCA layer is added between the two fully-connected layers, and the accuracy is guaranteed while the computational amount is reduced; and lastly, a logarithmic suppression method is provided for target location prediction, and a target location regression algorithm is improved. Finally, the purpose that the detection speed is accelerated while the detection precision is guaranteed is achieved. The method realizes accurately and quickly detecting and recognizing the targets of interest, and has a great application value.

Embodiments provide techniques, including systems and methods, for determining projected locations for providers to better match providers in response to a transport request. Providers may be matched to a requestor based not only on a current location of the provider with respect to a request location, with a projected location of the provider that accounts for timing delays in processing transport requests, communication networks, etc. As such, projecting the projected location of the provider allows the dynamic transportation matching system to be matched more efficiently, reducing delay for the provider and requestor, and improving the efficiency of the system by preventing provider system resources from being taken from other service areas and decreasing provider inefficient rerouting upon matching.

The invention relates to a time slot optimization-based double-layer satellite network routing method, so as to solve the problems that routing protocol overhead in the traditional method is large, an LEO satellite is hard to be connected with a GEO satellite in a polar region, and temporary link breaking of a polar orbit satellite between polar region orbits is not considered. The method comprises steps: (1) a 3GEO+66LEO double-layer satellite network model is built; (2) a covering domain grouping + orbit plane clustering-based satellite grouping management method is applied to determine a control relationship between an upper layer of satellites and a lower layer of satellites; (3) according to the control relationship between the upper layer of satellites and the lower layer of satellites, time slot division and optimization are carried out on a satellite operation cycle; (4) on the basis of location prediction routing algorithm, the GEO satellites calculate a routing table for the LEO satellites; and (5) the first step to the fourth step are combined, and routing updating is carried out when each time slice starts. The method belongs to the field of satellite communication.

The disclosure is directed toward a method for safely managing aircraft separation. The method comprises a data integration host configured for: receiving aircraft information from a first aircraft; receiving weather data from a weather monitoring system; combining the aircraft information of the first aircraft with the weather data; formulating a position prediction of a wake vortex located within a critical safety volume of a runway; receiving from a sensor real time wake vortex data in a path of the first aircraft; comparing the real time wake vortex data to the position prediction to validate the position prediction and to formulate a determination of whether the wake vortex is present in the critical safety volume; and utilizing the determination to transmit spacing data to air traffic control, wherein the spacing data is at least one of standard wake vortex spacing and minimum radar spacing.

The present application describes a computer-implemented method and system for obtaining position information for a moving mobile device with increased accuracy and reduced power consumption. The subject of the present application combines information from a GPS location sensor with information from MEMS devices such as an acceleration detector and a gyroscope using statistical analysis techniques such as a Kalman filter to estimate the location of the device with greater accuracy while using numerical methods such as the Newton-Raphson Method to minimize power consumption. Minimizing power consumption is possible because GPS signals sampled at a lower rate can conserve power, while GPS sampled at a lower rate and working together with MEMS devices can achieve the same level of location prediction accuracy as a GPS alone sampled at a higher rate.

Characteristics of criss-cross roads, non-uniform vehicle distribution and the like objectively exist in city roads, so that vehicle positions are easily changed to cause the problem of data transmission distortion of an internet-of-vehicles network layer, and the problem becomes a bottleneck hindering the development of internet-of-vehicles application services. An existing vehicle position prediction model is trained by generally utilizing historical track data of vehicles, so that consideration of complex vehicle states and real-time road condition information is lacking, and relationshipsbetween complex driving environments and vehicle driving behaviors and between the complex driving environments and vehicle position changes are mined insufficiently. For the problem, a deep learning-based method for building a position prediction model by considering vehicle driving influence factors in an internet-of-vehicles complex network comprehensively considers the vehicle driving influence factors such as vehicle body attributes, road information, driving environments and the like; in combination with a deep learning technology, the relationships between the vehicle driving influencefactors and the vehicle positions are mined; and the vehicle position prediction model is proposed, so that the purpose of improving vehicle position prediction accuracy is achieved, and assistance isprovided for improving the stability of route protocol design of the internet-of-vehicles network layer and effectively solving the data distortion problem.

A probabilistic prediction is made of the location of a wireless-enabled mobile station in a wireless local area network. The prediction includes calculating a vector representing movement of the mobile station through a space in which two or more access points of the network are located, and determining a region surrounding the vector in which the mobile station has at least a given probability to be located within a certain period of time.

The present invention provides a position estimation apparatus capable of determining whether an estimated value deviates from an actual value and trying a process to correct the estimated value if the estimated value is determined to be incorrect. The position estimation apparatus employs a position prediction unit configured to produce a prediction of the position of a mobile object having an odometry sensor mounted thereon, an environment observation unit configured to keep track of each feature point in the environment of the mobile object, a prediction-error check unit configured to determine whether the position prediction produced by the position prediction unit is correct or incorrect, a position-prediction correction unit configured to correct a wrong position prediction and a position / posture updating unit configured to update the position and / or posture of the mobile object on the basis of a correct position prediction.

The invention provides a trackless positioning navigation method and device. According to the method, the moving distance of a robot in a moving direction between adjacent time points (t-1) and t and the turning rate of the robot on a horizontal plane are measured, a position predication value of the robot is calculated through a preset dead reckoning equation and the measured moving distance and turning rate, local feature information in laser data obtained by measurement of a laser sensor is extracted and fused into a unified global feature map by the SLAM (Simultaneous Localization And Mapping) technology, and the processes of estimating the position of the robot and generating a topological graph are achieved, so that the positioning navigation stability and reliability are improved on the basis of reducing the cost and the operation and maintenance expenses, and the moving needs of the robot in a natural environment are met.

A cellular communications network comprises a plurality of geographically distributed access nodes arranged for communication with mobile terminals. A location prediction engine predicts a location for a first mobile terminal at a predetermined time and a configuration output configures two or more of the plurality of access nodes for coordinated communication with the first mobile terminal at the predetermined time, based on the predicted location for the first mobile terminal.

Embodiments are provided for traffic scheduling based on user equipment (UE) in wireless networks. A location prediction-based network scheduler (NS) interfaces with a traffic engineering (TE) function to enable location-prediction-based routing for UE traffic. The NS obtains location prediction information for a UE for a next time window comprising a plurality of next time slots, and obtains available network resource prediction for the next time slots. The NS then determines, for each of the next time slots, a weight value as a priority parameter for forwarding data to the UE, in accordance with the location prediction information and the available network resource prediction. The result for the first time slot is then forwarded from the NS to the TE function, which optimizes, for the first time slot, the weight value with a route and data for forwarding the data to the UE.

The invention relates to a k nearest neighbor search method for a variable weight network. The method comprises the steps as follows: routing thinking in a computer network is led in k nearest neighbor query, and predecessor / subsequent information in shortest path tree obtained in computation is taken as history data and is saved in a routing table by designing an update strategy of the routing table. Due to the dynamical update strategy of the routing table, the routing mechanism can adapt to dynamic changes of local road weights in a road net. The k nearest neighbor search method provides the candidate set screening rule and the road net area cutting rule of a non-mobile object as well as the position prediction method and the candidate set screening rule of a mobile object in the dynamical road net, effectively reduces the number of candidate points and narrows down the search range of the k nearest neighbor query, and avoids the defect that indexing and constraint conditions are adjusted frequently because the changes of a k nearest neighbors algorithm are caused by the road weights continuously in the conventional dynamical road net extended from a static road net.

Provided is a data preloading method which improves the response speed of a user device by predicting whether a particular object provided on a graphic user interface (GUI) will be selected based on the position of a pointer before the particular object is selected by a user and preloading data that should be loaded when the particular object is selected.

The invention discloses a method for wireless sensor network target tracking. The method comprises the following steps: to obtain quadratic polynomial predictive model of the relation between X coordinate or Y coordinate of description target and time; cluster header nodes predict the target position of next time by quadratic polynomial predictive model; cluster header arouse the sensor nodes near target predictive position to perform target tracking; two target predictive position and orientation positions perform self-adaptation on the value of the number of time according to the current hour and two adjoining hours. Cluster header nodes establish new quadratic polynomial predictive model using updated fitting data to realize the target tracking by repeated prediction and orientation course. The method has the advantages of effectively low the effect of the target acceleration change on target position prediction, decreases predictive error of target position forecast, improves target tracking accuracy.

The invention discloses a markov-model-based position prediction method. The method comprises: a historical track is collected, data centralization probabilities of all sampling positions are determined, normalization processing is carried out, discrete probability distribution is determined, and a variable-order global markov model is established; according to a historical track of each moving object, an individual markov model of each moving object is constructed; and on the basis of linear regression, the global markov model and the individual markov models are combined to generate a probability vector linear combination, time period division is carried out, all tracks are mapped to the time periods according time stamps, probabilities of falling into all time periods by all objects are calculated, clustering is carried out, and then a next position is predicted by combining a clustering result and the markov models. According to the invention, the time factor is taken into consideration and different models are trained at different time periods. When a next position is predicted, a proper model is selected based on the time stamp, so that the prediction accuracy is improved substantially.

The invention discloses a 'four-step' large-scale method for locating and detecting deep hydrothermal ore deposits or ore bodies. Deep ore deposits or ore bodies are directly located and detected through four stages, namely, ore deposit forming rule research, fine analysis of ore forming structure, altered lithofacies mapping and structural geochemical fine exploration, and geophysical deep detection and concealed ore location prediction. The method is based on a large-scale (1:200-1:50000) progressive deep-buried ore deposit or ore body location and detection technology integrating 'ore deposit model prediction, structure prediction, altered lithofacies mapping and structural geochemical fine exploration, and geophysical exploration'. A systematic deep ore exploration technology is realized.

The invention discloses a human body detection and tracking method and device based on an unmanned aerial vehicle mobile platform and belongs to the computer vision field. The technical key points of the method comprise a human body detector training step, a target human body recognizer offline training step, a target human body detection step and a human body tracking step, wherein the target human body detection step is characterized by receiving a current video frame shot by an unmanned aerial vehicle, extracting object characteristic value in the current video frame, sending the object characteristic value to a human body detector, the human body detector judging whether a human body is detected according to the characteristic value, and if so, further sending the characteristic value to a target human body recognizer, the target human body recognizer judging whether a target human body is detected according to the characteristic value, and if so, labeling the characteristic value and adding the characteristic value to a tracking list; and the human body tracking step is characterized by predicating position of the target human body in the next video frame according to the coordinate position of the target human body in the current video frame.

According to some embodiments of the present disclosure, the disclosure relates to an application system and server kit that create and serve digital twin-enabled applications. This disclosure also relates to a hub-and-spoke classification system. This disclosure also relates to a location-based services framework that leverages a generative content process to improve location prediction. This disclosure also relates to virtual reality and augmented reality applications, as well as digital agents that support various types of applications.

The invention provides a real-time visual object tracking method having shielding sensing mechanism, tracking results based on filter tracking algorithm based on correlation and Bayesian probability model tracking algorithm based on color statistical information are fused, position prediction of a target is performed, one-dimensional scale pyramid is independently trained to perform target scale estimation, in addition, a shielding sensing mechanism is designed, the mechanism can judge whether a target is partly or completely shielded, then an update strategy of the model and which kind of model utilized to track new frame are determined, tracker drifting caused by pollution of background information is avoided, and according to the above steps, the tracking algorithm is used for tracking the target persistently and iteratively. The above algorithm can be used for accurately tracking the target in real time, and a plurality of problems including deformation, rotation, shielding, size variation, illumination variation, motion blur and the like can be effectively handled.

The invention discloses a target specific response attention target tracking method based on a twin network, and relates to the computer vision technology. The method aims at overcoming the defect that an original target tracking method based on a twin network is not robust enough in complex tracking scenes such as rapid movement, shielding, rotation and background disorder of a target. The invention provides a target specific response attention target tracking method based on a twin network. The proposed target response attention module effectively weakens the influence of noise information on the tracking performance in the tracking process; meanwhile, the feature information having discrimination for the appearance change of the target object is enhanced, so that the target response graph generated by the twin network can be used for target position prediction, and therefore, more robust tracking performance can be realized. The method comprises five main parts: CNN feature extraction; generating a response graph through channel-by-channel cross-correlation; generating a weight by using an attention network, and weighting each channel response graph; and finally, determining a target position on the response graph, and providing a training method of the model.

A spatial prediction method capable of reducing the complexity of spatial prediction includes: detecting an edge (E) overlapping the current block by obtaining a horizontal gradient (Gy) and a vertical gradient (Gx) between pixels within a block adjacent to the current block; calculating an integer slope of the edge; determining, for each pixel position within the current block, a sub-pel position being an intersection between (i) a line that has the integer slope and passes through the pixel position and (ii) a boundary of the adjacent block; and predicting, for each pixel position, a pixel value at the pixel position based on a pixel value interpolated in the sub-pel position, wherein the boundary of the adjacent block is a row or a column that is the closest to the current block, among rows or columns of pixels included in the adjacent block.

A route display apparatus and method are provided. The apparatus includes a search unit that searches for a plurality of routes in which a plurality of destinations are stopped by in a different stopping order combination in each route. The apparatus also includes a prediction unit that predicts, for each of the routes, a travelable position that can be reached with a remaining amount of a battery. The prediction unit is configured to predict, for each of the routes, a necessity of charging based on whether the travelable position is on a side of a departure point in relation to a final destination. The apparatus also includes a display control unit that displays, for each of the routes, the necessity of charging in association with a corresponding stopping order for each of the routes, on a display part.

A method and system for segmenting multiple organs in medical image data is disclosed. A plurality of landmarks of a plurality of organs are detected in a medical image using an integrated local and global context detector. A global posterior integrates evidence of a plurality of image patches to generate location predictions for the landmarks. For each landmark, a trained discriminative classifier for that landmark evaluates the location predictions for that landmark based on local context. A segmentation of each of the plurality of organs is then generated based on the detected landmarks.

The invention discloses a meta-path-based link prediction method for aligned heterogeneous social networks, mainly solves the problem of link prediction (mainly including friend relationship prediction and position prediction) in the aligned heterogeneous social networks, and relates to related technologies of the aligned heterogeneous social networks, feature selection, link prediction and the like. The method mainly comprises six steps of (a) modeling the aligned heterogeneous social networks; (b) automatically extracting a meta-path; (c) defining a meta-path-based eigenvalue calculation method; (d) dividing data sets according to timestamps; (e) proposing a two-stage gradual forward greedy feature selection algorithm for performing feature selection; and (f) training a decision tree classifier based on a feature selection result to perform the link prediction. Based on the method, the aligned heterogeneous social networks consisting of Foursquare and Twitter are subjected to the link prediction; and the method has practical application values for entity recommendation, accurate marketing, criminal gang discovery and the like in the social networks.